Colon polyp image processing method and apparatus, and system

ABSTRACT

A colon polyp image processing method is provided. A value of a blood vessel color feature in an endoscopic image is classified by using an image classification model trained using a neural network algorithm to determine that the endoscopic image is a white light type picture or an endoscope narrow band imaging (NBI) type picture. A polyp in the endoscopic image is detected by using a polyp positioning model based on the determination that the endoscopic image is the white light type picture or the NBI type picture. A polyp type classification detection is performed on the detected polyp in the endoscopic image by using a polyp property identification model, and outputting an identification result.

RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/025,679 filed on Sep. 18, 2020, which is a continuation ofInternational Application No. PCT/CN2019/112788, filed on Oct. 23, 2019,which claims priority to Chinese Patent Application No. 201811287489.X,entitled “COLON POLYP IMAGE PROCESSING METHOD AND APPARATUS, AND SYSTEM”filed on Oct. 31, 2018. The entire disclosures of the prior applicationsare hereby incorporated by reference in their entirety.

FIELD OF THE TECHNOLOGY

Embodiments of this application relate to the field of computertechnologies, including a colon polyp image processing method andapparatus, and a system.

BACKGROUND OF THE DISCLOSURE

At present, the colon cancer ranks in the top five among high-occurrencemalignant tumors in China, and the incidence of the colon cancer inNorth America and Europe is also high. Colon cancer is a malignantdigestive tract tumor that often occurs in the colon. Generallyspeaking, 50% of patients with advanced colon cancer die of recurrenceand metastasis, and nearly 100% of patients with early colon cancer maybe completely cured. Therefore, it is necessary to prevent and curecolon cancer. However, the early colon cancer cannot be predicted byclinical symptoms.

In the related art, when identifying colon polyps, a method of slidingwindow is usually used for detecting a polyp image. The sliding windowmeans sliding an image block from top to bottom first and then from leftto right in an endoscopic video image frame. The position of the polypis manually marked. After the position of the polyp is determined, byusing a computer vision extraction method, an identification result isoutputted through classification.

In the sliding window method, it is calculated whether every image blockincludes a polyp by using a sliding window in an endoscopic video imageframe. Due to a large amount of image blocks, the amount of calculationis large and the real-time performance cannot meet requirements. Whenthe endoscope is controlled to move, an identification result of animage acquired in real time cannot be outputted in real time. Thereal-time performance of the manual marking method cannot meetrequirements. When the endoscope is controlled to move, anidentification result of an image acquired in real time cannot beoutputted in real time.

SUMMARY

Embodiments of this application provide a colon polyp image processingmethod and apparatus, and a system, to detect a position of a polyp inreal time and determine a property of the polyp, thereby improving theprocessing efficiency of a polyp image.

According to one aspect, an embodiment of this application provides acolon polyp image processing method. The method can include detecting,by a colon polyp image processing apparatus, a position of a polyp in ato-be-processed endoscopic image by using a polyp positioning model, andpositioning a polyp image block in the endoscopic image, the polyp imageblock a position region of the polyp in the endoscopic image. The methodcan further include performing, by the colon polyp image processingapparatus, a polyp type classification detection on the polyp imageblock by using a polyp property identification model, and outputting anidentification result.

According to another aspect, an embodiment of this application furtherprovides a colon polyp image processing apparatus. The apparatus caninclude processing circuitry that is configured to detect a position ofa polyp in a to-be-processed endoscopic image by using a polyppositioning model, and position a polyp image block in the endoscopicimage, the polyp image block including: a position region of the polypin the endoscopic image. The processing circuitry can be furtherconfigured to perform a polyp type classification detection on the polypimage block by using a polyp property identification model, and outputan identification result.

In the foregoing aspect, the composition modules of the colon polypimage processing apparatus may further perform steps described in theforegoing aspect and various possible implementations. For details,refer to the foregoing descriptions of the foregoing aspect and variouspossible implementations.

According to another aspect, an embodiment of this application furtherprovides a medical system, including an endoscope apparatus and a colonpolyp image processing apparatus, a communication connection beingestablished between the endoscope apparatus and the colon polyp imageprocessing apparatus. The endoscope apparatus being configured togenerate an endoscopic video stream, and transmit the generatedendoscopic video stream to the colon polyp image processing apparatus.The colon polyp image processing apparatus can be configured to receivethe endoscopic video stream from the endoscope apparatus, obtain ato-be-processed endoscopic image from the endoscopic video stream,detect a position of a polyp in a to-be-processed endoscopic image byusing a polyp positioning model, and position a polyp image block in theendoscopic image. The polyp image block can include a position region ofthe polyp in the endoscopic image. The apparatus can further beconfigured to perform polyp type classification detection on the polypimage block by using a polyp property identification model, and outputan identification result.

An embodiment of this application provides an image processing method.The method can include detecting, by an image processing apparatus, aposition of a target object in a to-be-processed image by using a targetobject positioning model, and positioning a target object image block inthe image, the target object image block including: a position region ofthe target object in the image. The method can further includeperforming, by the image processing apparatus, a target object typeclassification detection on the target object image block by using atarget object property identification model, and outputting anidentification result.

According to another aspect, an embodiment of this application providesa colon polyp image processing apparatus. The apparatus can include aprocessor and a memory. The memory can be configured to store aninstruction, and the processor being configured to execute theinstruction in the memory, to cause the colon polyp image processingapparatus to perform the method according to any one of the foregoingaspects.

Further, an embodiment of this application can provide a non-transitorycomputer-readable storage medium. The computer-readable storage mediumstoring an instruction that, when run on a computer, causes the computerto perform the method according to the foregoing aspects.

In an embodiment of this application, a position of a polyp in anendoscopic image is detected by using a polyp positioning model first,and a polyp image block is positioned in the endoscopic image. The polypimage block includes a position region of the polyp in the endoscopicimage. Finally, a polyp type classification detection is performed onthe polyp image block by using a polyp property identification model,and an identification result is outputted. In the embodiments of thisapplication, because the position of the polyp is detected by using thepolyp positioning model, the polyp image block may be directlypositioned in the endoscopic image. The classification detection for thepolyp type is also performed on the polyp image block, and does not needto be performed on the entire endoscopic image. Therefore, the real-timeperformance meets requirements. When the endoscope is controlled tomove, an identification result of an image acquired in real time can beoutputted in real time, thereby improving processing efficiency of thepolyp image.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the exemplary embodiments of thisapplication more clearly, the following briefly introduces theaccompanying drawings required for describing the embodiments. Theaccompanying drawings in the following description show only someexemplary embodiments of this application, and a person skilled in theart may still derive other accompanying drawings from the accompanyingdrawings.

FIG. 1 -a is a schematic structural diagram of compositions of a medicalsystem according to an embodiment of this application.

FIG. 1 -b is a schematic block flowchart of a colon polyp imageprocessing method according to an embodiment of this application.

FIG. 2 is a schematic diagram of an endoscopic image according to anembodiment of this application.

FIG. 3 is a schematic diagram of endoscopic images that are qualifiedpictures according to an embodiment of this application.

FIG. 4 is a schematic diagram of endoscopic images that areoverexposed/underexposed pictures with abnormal tone according to anembodiment of this application.

FIG. 5 is a schematic diagram of endoscopic images that are blurredpictures according to an embodiment of this application.

FIG. 6 -a is a schematic diagram of an endoscopic image that is a whitelight type picture according to an embodiment of this application.

FIG. 6 -b is a schematic diagram of an endoscopic image that is a narrowband imaging (NBI) type picture according to an embodiment of thisapplication.

FIG. 7 is a schematic diagram of a polyp image block circled on anendoscopic image according to an embodiment of this application.

FIG. 8 -a is a schematic structural diagram of compositions of a colonpolyp image processing apparatus according to an embodiment of thisapplication.

FIG. 8 -b is another schematic structural diagram of compositions of acolon polyp image processing apparatus according to an embodiment ofthis application.

FIG. 8 -c is another schematic structural diagram of compositions of acolon polyp image processing apparatus according to an embodiment ofthis application.

FIG. 8 -d is a schematic structural diagram of composition of a picturetype identification module according to an embodiment of thisapplication.

FIG. 8 -e is a schematic structural diagram of compositions of a polypclassification module according to an embodiment of this application.

FIG. 9 is a schematic structural diagram of compositions of a terminalto which a colon polyp image processing method is applied according toan embodiment of this application.

FIG. 10 is a schematic structural diagram of compositions of a server towhich a colon polyp image processing method is applied according to anembodiment of this application.

DESCRIPTION OF EMBODIMENTS

Exemplary embodiments of this application provide a colon polyp imageprocessing method and apparatus, and a system, to detect a position of apolyp in real time and determine a property of the polyp, therebyimproving the processing efficiency of a polyp image.

To make the inventive objectives, features, and advantages of theembodiments of this application clear and comprehensible, the followingclearly describes the technical solutions in the exemplary embodimentsof this application with reference to the accompanying drawings in theembodiments of this application. The embodiments described below aremerely some rather than all of the embodiments of this application. Allother embodiments obtained by a person skilled in the art based on theembodiments of this application shall fall within the protection scopeof this application.

In the specification, the claims, and the foregoing accompanyingdrawings of this application, the terms “include”, “have”, and any othervariations are meant to cover the non-exclusive inclusion, so that aprocess, method, system, product, or device that includes a list ofunits is not necessarily limited to those listed units, but may includeother units not expressly listed or inherent to such a process, method,product, or device.

An embodiment of the colon polyp image processing method in thisapplication may be specifically applied to a scene of processing a colonpolyp image in an endoscopic video stream. An identification result maybe output after the colon polyp image is processed according to thisembodiment of this application. The identification result may be usedfor helping a doctor discover a polyp in real time and determine aproperty of the polyp during an endoscopic examination, and guiding thedoctor to perform a next operation.

An embodiment of this application further provides a medical system. Asshown in FIG. 1 -a, the medical system 10 can include an endoscopeapparatus 20 and a colon polyp image processing apparatus 30. Further, acommunication connection can be established between the endoscopeapparatus 20 and the colon polyp image processing apparatus 30. Theendoscope apparatus 20 is configured to generate an endoscopic videostream and transmit the generated endoscopic video stream to the colonpolyp image processing apparatus 30.

The colon polyp image processing apparatus 30 is configured to receivethe endoscopic video stream from the endoscope apparatus 20, obtain ato-be-processed endoscopic image from the endoscopic video stream,detect a position of a polyp in the to-be-processed endoscopic image byusing a polyp positioning model, and position a polyp image block in theendoscopic image. The polyp image block includes a position region ofthe polyp in the endoscopic image. The colon polyp image processingapparatus 30 can further perform a polyp type classification detectionon the polyp image block by using a polyp property identification model,and output an identification result.

The medical system provided by this embodiment of this applicationincludes an endoscope apparatus and a colon polyp image processingapparatus. The endoscopic video stream may be transmitted between theendoscope apparatus and the colon polyp image processing apparatus in awired or wireless manner. The endoscope apparatus may take images of thecolon in a patient through the endoscope, to generate the endoscopicvideo stream. The colon polyp image processing apparatus detects theposition of the polyp by using the polyp positioning model, so that thepolyp image block may be directly positioned in the endoscopic image.The polyp type classification detection is also performed on the polypimage block, but does not need to be performed on the entire endoscopicimage, so that the real-time performance meets requirements. When theendoscope is controlled to move, an identification result of an imageacquired in real time may be outputted in real time, thereby improvingprocessing efficiency of the polyp image.

Referring to FIG. 1 -b, a colon polyp image processing method providedin an embodiment of this application may include the following steps.

In step 101, a colon polyp image processing apparatus detects a positionof a polyp in a to-be-processed endoscopic image by using a polyppositioning model, and positions a polyp image block in the endoscopicimage. The polyp image block includes a position region of the polyp inthe endoscopic image.

In this embodiment of this application, the to-be-processed endoscopicimage may be a single frame of endoscopic image obtained from theendoscopic video stream by the colon polyp image processing apparatus,or may be a single frame of endoscopic image received from an endoscopeapparatus by the colon polyp image processing apparatus. After obtainingthe single frame of endoscopic image, a position of a polyp position inthe endoscopic image is detected by using a polyp positioning modeltrained in advance. The polyp positioning model includes networkparameters that have been trained, and it may be detected, by using thenetwork parameters of the polyp positioning model, which image regionsin the endoscopic image meet polyp features, thereby determining theposition region that meets the polyps features as the polyp image blockcircled in the endoscopic image in this embodiment of this application.

FIG. 7 is a schematic diagram of a polyp image block circled in anendoscopic image according to an embodiment of this application. Thepolyp image block includes the position region of the polyp in theendoscopic image. The completed polyp positioning model trained inadvance is used in this embodiment of this application, and the polypimage block may be quickly circled through model detection, ensuringthat the polyp image block may be determined in real time after theendoscopic video stream is generated, and ensuring that the polyp typeclassification detection may be performed in real time.

In some embodiments of this application, the endoscopic image may beclassified as a white light type picture or an NBI type pictureaccording to different picture types. Therefore, the polyp positioningmodel trained in advance also needs to be divided into a white lightpolyp positioning model and an NBI polyp positioning model. The whitelight polyp positioning model can be obtained in a manner that the colonpolyp image processing apparatus performs polyp position training on theoriginal polyp positioning model through white light type picturetraining data by using a neural network algorithm. The NBI polyppositioning model can be obtained in a manner that the colon polyp imageprocessing apparatus performs polyp position training on the originalpolyp positioning model through NBI type picture training data by usingthe neural network algorithm.

In this embodiment of this application, first, training data for thewhite light type and the NBI type are obtained in advance, that is, thewhite light type picture training data and the NBI type picture trainingdata are obtained. A polyp positioning model is obtained in advancethrough training using a neural network algorithm. The polyp positioningmodel may be trained by using a plurality of machine learningalgorithms. For example, the polyp positioning model may be a deepneural network model, a cyclic neural network model or the like. Forexample, the polyp positioning model may be trained by using a YOLOv2algorithm.

In some embodiments of this application, in an implementation scenewhere the polyp positioning model is divided into the white light polyppositioning model and the NBI polyp positioning model, the foregoingstep 101 that a colon polyp image processing apparatus detects aposition of a polyp in a to-be-processed endoscopic image by using apolyp positioning model, and positions a polyp image block in theendoscopic image can further include positioning the polyp by using thewhite light polyp positioning model in a case that the endoscopic imageis the white light type picture, to position a white light polyp imageblock in the endoscopic image, and positioning the polyp by using theNBI polyp positioning model in a case that the endoscopic image is theNBI type picture, to position an NBI polyp image block in the endoscopicimage.

In this embodiment of this application, it is necessary to determine thespecific position of the polyp in the endoscopic image, to provide inputdata for the next operation of polyp property identification.Considering the requirements on real-time performance, in thisembodiment of this application, the position of the polyp is detected byusing the YOLOv2 algorithm. A principle and an implementation of YOLOv2are described below. The YOLOv2 is a joint training method for detectionand classification. A YOLO9000 model is trained based on a COCOdetection data set and an ImageNet classification data set by using thejoint training method, and the model can detect more than 9000 types ofobjects. YOLOv2 are improved in many aspects compared with YOLOv1, sothat performance of YOLOv2 is remarkably improved, and the speed ofYOLOv2 is still very fast. The YOLOv2 algorithm is an upgraded versionof a YOLO algorithm, and is an end-to-end real-time target detection andrecognition algorithm. By using a single neural network, the algorithmtransforms a target detection problem into extraction of bounding boxesin images and a regression problem of category probabilities. Comparedwith YOLO, the YOLOv2 algorithm uses a multi-scale training method andborrows the concept of Faster RCNN anchor box, thus not only ensuring adetection speed, but also greatly improving the accuracy andgeneralization ability of model detection.

The YOLOv2 algorithm is applied to a polyp positioning task in thisembodiment of this application, a detection target is a colon polyp, anda size of the anchor box is obtained through clustering according tobuilt-in polyp training data. A transfer learning technology is used inalgorithm training. Transfer learning refers to applying matureknowledge in a field to other scenes, and in terms of a neural network,it means transferring a weight of each node in network layers from atrained network to a brand new network instead of starting from scratch,and it is unnecessary to train a neural network for each specific task.Parameters trained by using an open-source, large-scale, labeled dataset are used for initialization. For example, the data set may beImagenet data. The Imagenet data is an open source data set related toimage classification and target detection in the field of computervision. The Imagenet data covers tens of thousands of categories, andhas a data volume of more than one million. Using model initializationparameters trained by a large-scale data set may better allow a model toconverge to a global optimal solution.

In an image classification model, white light type pictures and NBI typepictures may be distinguished. The two types of images differ greatly interms of polyp appearances. A flow direction of a blood vessel may beobserved in the NBI type picture, and the color of the blood vessel isblack in the NBI type picture. Therefore, it is necessary to trainrespective polyp positioning models for the white light picture data andthe NBI picture data, which are referred to as a white light polyppositioning model and an NBI polyp positioning model. The two polyppositioning models are both trained by using the method described above,and the only difference is training data of the models. The trainingdata of the white light polyp positioning model is white light typepictures, and the training data of the NBI polyp positioning model isNBI type pictures. In a process of the algorithm, when a previous moduledetermines an image as a white light type picture, the white light polyppositioning model is called to position the polyp; otherwise, the NBIpolyp positioning model is called to position the polyp. The circledpolyp image block is outputted in a case that the polyp is positioned,to be used as an input of a polyp property identification model.

Before the foregoing step 101, the colon polyp image processing methodprovided in this embodiment of this application may further include thefollowing step 100. In step 100, the colon polyp image processingapparatus obtains the to-be-processed endoscopic image from anendoscopic video stream. In this embodiment of this application, when adoctor operates an endoscope to examine the colon, the endoscopeapparatus may generate an endoscopic video stream, where the endoscopicvideo stream includes successive frames of endoscopic images. After theendoscope apparatus generates the endoscopic video stream, theendoscopic video stream may be transmitted to the colon polyp imageprocessing apparatus. The colon polyp image processing apparatus mayreceive the endoscopic video stream from the endoscope apparatus, andobtain a single frame of endoscopic image from the endoscopic videostream. For each frame of endoscopic image, the polyp position and polyptype may be identified according to the method provided in thisembodiment of this application, so that the property of the colon polypin the endoscopic video stream may be identified in real time. When thedoctor operates the endoscope to examine the colon, the position of thecolon polyp in the video stream may be positioned in real time and theproperty of the polyp may be determined. If the polyp is identified as anon-adenomatous polyp, the doctor does not need to remove the polyp forpathological examination. Processing the endoscopic image of each frameaccording to this embodiment of this application may help the doctor tofind the polyp in real time and prevent missed diagnosis of the polyp,and may also help the doctor to determine the property of the polyp, sothat the doctor determines the polyp more accurately. In the subsequentsteps, image processing may be performed on the endoscope image in thesingle frame to output an identification result. For the processing ofendoscope images in other frames in the endoscopic video stream, referto the foregoing processing procedure, which is only explained herein.

FIG. 2 is a schematic diagram of an endoscopic image according to anexemplary embodiment of this application. After the endoscopic videostream is generated, one frame of endoscopic image is extracted from theendoscopic video stream. In the picture shown in FIG. 2 , the endoscopicimage is a colon image shown in the box, parameters of the endoscope areshown on the left side of the endoscopic image, and parameter values ofthe endoscope may be set according to an actual scene. The parameters ofthe endoscope have nothing to do with the image processing. Therefore,only a colon image region may be reserved after the endoscopic videostream is acquired.

In algorithms designed in the related art, it is necessary to manuallyfilter out low-quality noise data. However, the algorithms in therelated art cannot be used in an actual production environment. Becausethe low-quality noise data is filtered out manually, the designedalgorithms have a good effect in an ideal environment, but cannot beused in an actual scene. In order to resolve this problem, in someembodiments of this application, after step 100 of obtaining theto-be-processed endoscopic image from the endoscopic video stream, themethod provided in this embodiment of this application further includesthe following steps.

The colon polyp image processing apparatus extracts a color feature, agradient variation feature and an abnormal brightness feature from theendoscopic image. Further, the colon polyp image processing apparatuscan determine whether the endoscopic image is a low-quality pictureaccording to the color feature, the gradient variation feature and theabnormal brightness feature, where the low-quality picture includes ablurred picture, an overexposed/underexposed picture with abnormal tone,and a low-resolution picture.

The following step 101 is triggered in a case that the endoscopic imageis not the low-quality picture. The colon polyp image processingapparatus detects the position of the polyp position in theto-be-processed endoscopic image by using the polyp positioning model.

The low-quality picture may also be referred to as a low-qualitypicture. For the endoscopic image in a single frame in the input videostream, it is determined whether the endoscopic image is a low-qualitypicture; if the endoscopic image is the low-quality picture, theendoscopic image is directly filtered out and the subsequent moduleidentification is skipped. In an actual production environment, thereare a large number of blurred pictures and fecal water pictures causedby underprepared intestinal, which affect the subsequent polyppositioning and an algorithm effect of a property identification module.Therefore, in this embodiment of this application, the color feature,the gradient variation feature and the abnormal brightness feature maybe extracted to detect, based on the three extracted features, whetherthe endoscopic image is the low-quality picture.

The low-quality picture defined in this embodiments of this applicationincludes three categories blurred picture, overexposed/underexposedpicture with abnormal tone, and low-resolution picture. FIG. 3 is aschematic diagram of endoscopic images that are a qualified picturesaccording to an embodiment of this application. The two pictures on theleft and right shown in FIG. 3 are both qualified pictures. Thequalified pictures refer to pictures other than the blurred picture, theoverexposed/underexposed picture with abnormal tone, and thelow-resolution picture. FIG. 4 is a schematic diagram of endoscopicimages that are overexposed/underexposed pictures with abnormal toneaccording to an embodiment of this application. Abnormal colors occur inthe both left and right pictures shown in FIG. 4 , and therefore, thepictures are unqualified pictures. FIG. 5 is a schematic diagram ofendoscopic images that are blurred pictures according to an embodimentof this application. Both the left and right pictures shown in FIG. 5are blurred, and therefore are unqualified pictures. Specificidentification processes of a blurred picture, anoverexposed/underexposed picture with abnormal tone, and alow-resolution picture will be illustrated respectively below withexamples.

Identification of a low-resolution picture may be achieved bycalculating an effective pixel area in the picture. The effective pixelarea refers to an area after black borders on upper, lower, left andright sides of the picture are removed through cropping, as shown by thearea enclosed by a white box in FIG. 2 . A black border croppingalgorithm is mainly to collect statistics about gray-scale valuedistribution of pixel values in each row or each column. If a ratio ofgray or black pixel values in a row or column is greater than a certainvalue, it is considered that the row or column needs to be removedthrough cropping. If the effective area after the black borders areremoved through cropping is less than a certain threshold, the pictureis considered to be a low-resolution picture, where the threshold may becustomized according to actual applications.

A detection algorithm for a blurred picture can be performed as follows.

(1) A Gaussian filtering operation with a standard deviation sigma=2.5is performed on an input image, to eliminate moiré generated in imagesampling.

(2) An original image is defined as R, and an image P is obtained aftera median filtering operation with a pixel value of 3*3 is performed.

(3) Gradients of the image P and image R are calculated respectively,and a gradient map G_P of the median filtered image and a gradient mapG_R of the original image are obtained by using a Sobel edge detectionoperator. G_P and G_R highlight details of the image edges and enhancethe image edges.

(4) A similarity between G_P and G_R is calculated. For example, aclassification model estimation method, such as an algorithm similar toF-Score, may be used for screening. For a more blurred image, G_P andG_R have a higher similarity.

Finally, whether the endoscopic image is a blurred picture may bedetermined according to the similarity between G_P and G_R.

In a detection algorithm for an overexposed/underexposed picture withabnormal tone, there are numerous abnormal types, which can hardly beexhausted. Therefore, a standard library file for qualified tones andnormal shooting is created. A detection algorithm can be performed asfollows

(1) An image is divided into 7*7 image blocks and nine image blocks areobtained.

(2) Hue (H), saturation (S), and value (V) of each image block arecalculated in a Hue, Saturation, Value (HSV) space.

(3) H and S are used as features to match with H and S of a standardimage respectively, a similarity threshold t is set, and it iscalculated whether each image block of the image is similar to thestandard library.

(4) Matching degree similarity results of the nine image blocks areaccumulated, where a cumulative value is incremented by 1 when thematching degree is greater than the threshold t. When the cumulativevalue is greater than 5, the image is considered as a target tonematching image, and a returned detection result is True.

An endoscopic image that meets the foregoing target tone matching resultmay be determined as an overexposed/underexposed picture with abnormaltone.

In some embodiments of this application, the endoscopic video stream maybe generated in a plurality of shooting methods. Therefore, theendoscopic images in the endoscopic video stream may include a pluralityof picture types according to different shooting methods. Differentpolyp positioning models need to be used for different picture typesduring the polyp position detection, and details are described in thesubsequent embodiments.

After step 100 of obtaining the to-be-processed endoscopic image fromthe endoscopic video stream, the method provided in this embodiment ofthis application further include that the colon polyp image processingapparatus identifies a picture type of the endoscopic image, anddetermines that the endoscopic image is a white light type picture or anNBI type picture.

According to different shooting methods used for the endoscopic videostream, the endoscopic image extracted from the endoscopic video streammay also have different picture types. For example, the endoscopic imagemay be a white light type picture or an NBI type picture. FIG. 6 -a is aschematic diagram of an endoscopic image that is a white light typepicture according to an embodiment of this application. The white lighttype picture refers to a red, green and blue (RGB) image that is imagedby an ordinary light source. FIG. 6 -b is a schematic diagram of anendoscopic image that is an NBI type picture according to an embodimentof this application. In the NBI type picture, a broad band spectrum ofRGB light waves emitted by the endoscope light source is removed by afilter, and only a narrow band spectrum is reserved for diagnosingvarious digestive tract diseases.

Further, in some embodiments of this application, the identifying apicture type of the endoscopic image, and determining that theendoscopic image is a white light type picture or an NBI type picturecan include performing classification training on an original imageclassification model through white light type picture training data andNBI type picture training data by using a neural network algorithm, toobtain a trained image classification model. It can further includeextracting a blood vessel color feature from the endoscopic image byusing the trained image classification model, and classifying a value ofthe blood vessel color feature by using the trained image classificationmodel, to obtain that the endoscopic image is the white light typepicture or the NBI type picture.

In this embodiment of this application, first, training data for a whitelight type and an NBI type are obtained respectively, that is, whitelight type picture training data and NBI type picture training data areobtained. An image classification model is trained in advance by using aneural network algorithm, and the image classification model may betrained by using a plurality of machine learning algorithms. Forexample, the image classification model specifically may be a deepneural network (DNN) model, or a cyclic neural network model. Forexample, the deep neural network model may be densely connectedconvolutional networks (DenseNet). After the white light type picturetraining data and the NBI type picture training data are collected inadvance model training is performed through the white light type picturetraining data and the NBI type picture training data, a trained imageclassification model is outputted.

After the training of the image classification model is completed, ablood vessel color feature is extracted from the endoscopic image byusing the trained image classification model, and the blood vesselscolor feature is a basis for classification of the endoscopic image.Finally, a value of the blood vessels color feature is classified byusing the trained image classification model to obtain that theendoscope image is the white light type picture or the NBI type picture.

In this embodiment of this application, an input of the imageclassification model is a qualified single frame of endoscopic image,and the image classification model outputs a result indicating whetherthe endoscopic image is a white light type picture or an NBI typepicture. When a doctor actually operates an endoscope to examine thecolon, if a suspected polyp is found, a pathological type of the currentpolyp is generally diagnosed in an NBI mode. A picture in the NBI modemay show a direction of the blood vessel more clearly. FIG. 6 -a shows awhite light type picture and FIG. 6 -b shows an NBI type picture. Forexample, the image classification model in this embodiment of thisapplication may classify and detect a picture type by using DenseNet.Certainly, other picture classification networks may also be used inthis embodiment of this application to achieve similar functions;however, the identification effect may differ in some degree, which isnot limited herein.

Execution of the image classification model may be converted into animage classification problem. An image classification algorithm used isthe DenseNet. A size of an input image of the networks is 224*224.Therefore, an inputted original picture is scaled to a fixed size of224*224 first. Considering that a task of the image classification modelprefers lower-level feature combinations, for example, blood vesselcolor and the like, a wider and shallower mode is used when thecombination of depth and width of the DenseNet structure is designed.The final network structure used is DenseNet-40, where 40 refers to thenumber of network layers. A growth-rate is set to 48 through networkparameter optimization, and a compression ratio of features through atransition layer is 0.5, thereby achieving an optimal effect. A modelstructure is shown in the following Table 1.

Network layer settings Layers Output Size (DenseNet-40) Convolution 112× 112 7 × 7 conv, stride 2 Pooling 56 × 56 3 × 3 max pool, stride 2Dense Block (1) 56 × 56 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 6$ Transition Layer (1) 56 × 56 1 × 1 conv 28 × 282 × 2 average pool, stride 2 Dense Block (2) 28 × 28 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 6$ Transition Layer (2) 28 × 28 1 × 1 conv 14 × 142 × 2 average pool, stride 2 Dense Block (3) 14 × 14 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 6$ Classification Layer 1 × 1 7 × 7 global averagepool 2D fully-connected, softmax classifier

In the embodiment shown in the foregoing Table 1, the functionimplementation and execution process of each layer in DenseNet-40 may bedetermined according to scenes. In addition, cony in the network layersincludes three operations: batch normalization (batchnorm), activationlayer (ReLU) and a convolution layer.

In step 102, the colon polyp image processing apparatus can perform apolyp type classification detection on the polyp image block by using apolyp property identification model, and outputs an identificationresult. In this embodiment of this application, after the polyp imageblock is circled in the endoscopic image, next, it is only necessary toperform a polyp type classification detection on the polyp image blockby using the polyp property identification model trained in advance, andoutput the identification result. The identification result may output apolyp type with a maximum probability, and may also output polyp typesunder various confidence conditions, where the confidence is acredibility of the polyp image block including various polyp types aftera prediction is performed based on the polyp property identificationmodel.

In this embodiment of this application, the polyp propertyidentification model may perform a polyp property discrimination task,which is implemented, for example, through an image classification task,and an input is picture data of a positioning box outputted by the polyppositioning model. As shown in FIG. 7 , the polyp image block circled inthe endoscopic image is the polyp detected by the polyp positioningmodel, and is used as input data of the polyp property identificationmodel. A module output may be four class values (0, 1, 2, 3), where 0means that the region has no polyps and is normal, 1 represents anon-adenomatous polyp, 2 represents an adenomatous polyp, and 3represents an adenocarcinoma. In addition, respective confidenceconditions may be set for a normal region, a non-adenomatous, anadenomatous, and an adenocarcinoma. If the output is 0, a determiningresult of the polyp positioning model is corrected, this region has nopolyps and is a normal region.

In some embodiments of this application, the step 102 where the colonpolyp image processing apparatus performs a polyp type classificationdetection on the polyp image block by using a polyp propertyidentification model, and outputs an identification result can includeperforming polyp type classification detection training on an originalpolyp property identification model through polyp picture training dataof different polyp types by using a neural network algorithm, to obtaina trained polyp property identification model. The step 102 can furtherinclude extracting a polyp type feature from the polyp image block byusing the trained polyp property identification model, and classifying avalue of the polyp type feature by using the trained polyp propertyidentification model, and outputting the identification result.

In this embodiment of this application, the polyp picture training dataof different polyp types is obtained first. The polyp propertyidentification model is obtained in advance through training using theneural network algorithm, and the polyp property identification modelmay be trained by using a plurality of machine learning algorithms. Forexample, the polyp property identification model may be a deep neuralnetwork model or a cyclic neural network model. For example, the deepneural network model may be DenseNet. After the polyp picture trainingdata of different polyp types is collected in advance and model trainingis performed through the polyp picture training data of different polyptypes, the trained polyp property identification model is outputted.

After the training of the polyp property identification model iscompleted, the polyp type feature is extracted from the polyp imageblock by using the trained polyp property identification model, and thepolyp type feature is a basis for classification of the polyp imageblock. Finally, the value of the polyp type feature is classified byusing the trained polyp property identification model, to obtain theidentification result.

In some embodiments of this application, after step 102 that the colonpolyp image processing apparatus positions the polyp image block in theendoscopic image, the method provided in this embodiment of thisapplication further can include the following steps that the colon polypimage processing apparatus expands a polyp region occupied by the polypimage block in the endoscopic image upwards, downwards, leftwards andrightwards according to a preset image expansion ratio, to obtain anexpanded polyp image block, and the colon polyp image processingapparatus inputs the expanded polyp image block into the polyp propertyidentification model.

In this embodiment of this application, the polyp propertyclassification task of the polyp property identification model may beimplemented by using a DenseNet algorithm. The algorithm requires inputimages to have the same size. However, polyp positions outputted by thepolyp positioning model has different sizes. During construction ofalgorithm input data, the method used in this embodiment of thisapplication is as follows: for the polyp image block outputted by thepolyp positioning model, expanding the region by 10% upwards, downwards,leftwards and rightwards to ensure the framed region has contextsemantic information, to help the subsequent polyp propertyidentification model to extract features. The expanded region isdirectly normalized to an input size of 224*224 required by the model.Considering the complexity of the task, deeper DenseNet may be used. Thefinal network structure used is DenseNet-121. A growth-rate is set to 24through the network parameter optimization, and a compression ratio offeatures through a transition layer is 0.5, thereby achieving an optimaleffect. A model structure is shown in the following Table 2.

Network layer settings Layers Output Size (DenseNet-121) Convolution 112× 112 7 × 7 conv, stride 2 Pooling 56 × 56 3 × 3 max pool, stride 2Dense Block (1) 56 × 56 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 6$ Transition Layer (1) 56 × 56 1 × 1 conv 28 × 282 × 2 average pool, stride 2 Dense Block (2) 28 × 28 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 12$ Transition Layer (2) 28 × 28 1 × 1 conv 14 × 142 × 2 average pool, stride 2 Dense Block (3) 14 × 14 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 24$ Transition Layer (3) 14 × 14 1 × 1 conv 7 × 7 2× 2 average pool, stride 2 Dense Block (4) 7 × 7 $\begin{bmatrix}{1 \times 1{conv}} \\{3 \times 3{conv}}\end{bmatrix} \times 16$ Classification Layer 1 × 1 7 × 7 global averagepool 2D fully-connected, softmax classifier

Finally, according to the polyp image processing method provided in thisembodiment of this application, it takes about 100 milliseconds (ms) toprocess each frame of endoscopic image, which meets requirements onreal-time performance. Compared with doctors of different levels, thealgorithm effect is equivalent to the level of top-notch doctors. Whendeployed in primary hospitals, the method may help doctors to find andidentify polyps in real time.

In this embodiment of this application, the method may help a doctor tofind a polyp and determine a property of the polyp in real time when thedoctor conducts an endoscopic examination. The method may prevent thedoctor from missing diagnosis of the polyp, and help the doctor toimprove the accuracy of polyp property identification. If the polyp isidentified a non-adenomatous polyp with high confidence, the doctor doesnot need to remove the polyp for pathological examination, which mayreduce the operation time of the doctor, thereby further reducing a highcomplication risk of the patient and diagnosis cost of the patient, andreducing the burden of an endoscopist and a pathologist.

As can be learned from the description of the foregoing embodiments ofthis application, a position of a polyp in an endoscopic image is firstdetected by using a polyp positioning model, and a polyp image block ispositioned in the endoscopic image, where the polyp image blockincludes: a position region of the polyp in the endoscopic image.Finally, a polyp type classification detection is performed on the polypimage block by using a polyp property identification model, and anidentification result is outputted. In the embodiments of thisapplication, because the position of the polyp is detected by using thepolyp positioning model, the polyp image block may be directlypositioned in the endoscopic image. The classification detection for thepolyp type is also performed on the polyp image block, and does not needto be performed on the entire endoscopic image. Therefore, the real-timeperformance meets requirements. When the endoscope is controlled tomove, an identification result of an image acquired in real time can beoutputted in real time, thereby improving processing efficiency of thepolyp image.

The foregoing method embodiments are expressed as a series of actioncombinations for the purpose of brief description, but it is to belearned by a person skilled in the art that, the embodiments of thisapplication are not limited to the described action sequence becausesome steps may be performed in other sequences or simultaneouslyaccording to the exemplary embodiments of this application. In addition,it is to be also learned by a person skilled in the art that theembodiments described in this specification are all preferredembodiments, and the related actions and modules are not necessarilymandatory in the embodiments of this application.

For the convenience of better implementation of the foregoing solutionsof the embodiments of this application, the following further provides arelated apparatus configured to implement the foregoing solutions.

Referring to FIG. 8 -a, an embodiment of this application provides acolon polyp image processing apparatus 800. The apparatus may includeone or more processors and one or more memories storing a program unit,the program unit being executed by the processor. The program unitincludes a position detection module 801 and a polyp classificationmodule 802. Of course, it should be understood that one or more of themodules described in this specification may be implemented by processingcircuitry.

The position detection module 801 is configured to detect a position ofa polyp in a to-be-processed endoscopic image by using a polyppositioning model, and position a polyp image block in the endoscopicimage, the polyp image block including a position region of the polyp inthe endoscopic image.

The polyp classification module 802 is configured to perform a polyptype classification detection on the polyp image block by using a polypproperty identification model, and output an identification result.

In some embodiments of this application, as shown in FIG. 8 -a, thecolon polyp image processing apparatus 800 may further include an imageobtaining module 803. The image obtaining module 803 is configured toobtain the to-be-processed endoscopic image from an endoscopic videostream.

In some embodiments of this application, as shown in FIG. 8 -b, thecolon polyp image processing apparatus 800 can further include alow-quality picture identification module 804, configured to extract acolor feature, a gradient variation feature and an abnormal brightnessfeature from the endoscopic image before the position detection module801 detects the position of the polyp in the to-be-processed endoscopicimage by using the polyp positioning model, determine whether theendoscopic image is a low-quality picture according to the colorfeature, the gradient variation feature and the abnormal brightnessfeature. The low-quality picture includes a blurred picture, anoverexposed/underexposed picture with abnormal tone, and alow-resolution picture, and trigger the position detection module in acase that the endoscopic image is not the low-quality picture.

In some embodiments of this application, referring to FIG. 8 -c, thecolon polyp image processing apparatus 800 further includes a picturetype identification module 805, configured to identify a picture type ofthe endoscopic image before the position detection module 801 detectsthe position of the polyp in the to-be-processed endoscopic image byusing the polyp positioning model, and determine that the endoscopicimage is a white light type picture or an endoscope NBI type picture.

In some embodiments of this application, referring to FIG. 8 -d, thepicture type identification module 805 includes an image classificationmodel training unit 8051, configured to perform classification trainingon an original image classification model through white light typepicture training data and NBI type picture training data by using aneural network algorithm, to obtain a trained image classificationmodel. The picture type identification module 805 can further include ablood vessel color feature extraction unit 8052, configured to extract ablood vessel color feature from the endoscopic image by using thetrained image classification model to, and a picture classification unit8053, configured to classify a value of the blood vessel color featureby using the trained image classification model, to obtain that theendoscopic image is the white light type picture or the NBI typepicture.

In some embodiments of this application, the polyp positioning modelincludes a white light polyp positioning model and an NBI polyppositioning model. Further, the white light polyp positioning model isobtained in the following manner: the colon polyp image processingapparatus performs polyp position training on the original polyppositioning model through the white light type picture training data byusing the neural network algorithm. Additionally, the NBI polyppositioning model can be obtained by the colon polyp image processingapparatus perform polyp position training on the original polyppositioning model through the NBI type picture training data by usingthe neural network algorithm.

In some embodiments of this application, the position detection module801 is specifically configured to perform polyp positioning by using thewhite light polyp positioning model in a case that the endoscopic imageis the white light type picture, to position a white light polyp imageblock in the endoscopic image, and perform polyp positioning by usingthe NBI polyp positioning model in a case that the endoscopic image isthe NBI type picture, to position an NBI polyp image block in theendoscopic image.

In some embodiments of this application, referring to FIG. 8 -e, thepolyp classification module 802 includes a polyp property identificationmodel training unit 8021 that is configured to perform polyp typeclassification detection training on an original polyp propertyidentification model through polyp picture training data of differentpolyp types by using a neural network algorithm, to obtain a trainedpolyp property identification model. The polyp classification module 802can further include a polyp type feature extraction unit 8022 that isconfigured to extract a polyp type feature from the polyp image block byusing the trained polyp property identification model, and a polypclassification unit 8023 that is configured to classify a value of thepolyp type feature by using the trained polyp property identificationmodel, and output the identification result.

As can be learned from the description of the foregoing embodiments ofthis application, a position of a polyp in an endoscopic image is firstdetected by using a polyp positioning model, and a polyp image block ispositioned in the endoscopic image, where the polyp image block includesa position region of the polyp in the endoscopic image. Finally, a polyptype classification detection is performed on the polyp image block byusing a polyp property identification model, and an identificationresult is outputted. In the embodiments of this application, because theposition of the polyp is detected by using the polyp positioning model,the polyp image block may be directly positioned in the endoscopicimage. The classification detection for the polyp type is also performedon the polyp image block, and does not need to be performed on theentire endoscopic image. Therefore, the real-time performance meetsrequirements. When the endoscope is controlled to move, anidentification result of an image acquired in real time can be outputtedin real time, thereby improving processing efficiency of the polypimage.

An exemplary embodiment of this application further provides anotherterminal. As shown in FIG. 9 , for ease of description, only partsrelated to the embodiments of this application are shown. For specifictechnical details that are not disclosed, refer to the method part inthe embodiments of this application. The terminal may be any terminaldevice including a mobile phone, a tablet computer, a personal digitalassistant (PDA), a point of sales (POS), an on-board computer and thelike, and the terminal being a mobile phone is used as an example.

FIG. 9 is a block diagram of a partial structure of a mobile phonerelated to a terminal according to an embodiment of this application.Referring to FIG. 9 , the mobile phone includes components such as aradio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, adisplay unit 1040, a sensor 1050, an audio circuit 1060, a Wi-Fi module1070, a processor 1080, and a power supply 1090. A person skilled in theart may understand that the structure of the mobile phone shown in FIG.9 does not constitute a limitation to the mobile phone, and the mobilephone may include more components or fewer components than those shownin the figure, or some components may be combined, or a differentcomponent deployment may be used.

The components of the mobile phone are described in detail below withreference to FIG. 9 . The RF circuit 1010 may be configured to receiveand transmit signals during an information receiving and transmittingprocess or a call process. Specifically, the RF circuit receivesdownlink information from a base station, then delivers the downlinkinformation to the processor 1080 for processing, and transmits designeduplink data to the base station. Usually, the RF circuit 1010 includes,but is not limited to, an antenna, at least one amplifier, atransceiver, a coupler, a low noise amplifier (LNA), and a duplexer. Inaddition, the RF circuit 1010 may also communicate with a network andanother device through wireless communication. The wirelesscommunication may use any communications standard or protocol,including, but not limited to a global system of mobile communication(GSM), a general packet radio service (GPRS), code division multipleaccess (CDMA), wideband code division multiple access (WCDMA), Long TermEvolution (LTE), an email, a short messaging service (SMS), and thelike.

The memory 1020 may be configured to store a software program andmodule. The processor 1080 runs the software program and module storedin the memory 1020, to implement various functional applications of themobile phone and data processing. The memory 1020 may mainly include aprogram storage area and a data storage area. The program storage areamay store an operating system, an application program required for atleast one function, such as an audio playing function, an image playingfunction, and the like. The data storage area may store data, such asaudio data, a phone book, and the like, created according to use of themobile phone. In addition, the memory 1020 may include a high speedrandom access memory, and may further include a non-volatile memory,such as at least one magnetic disk memory device, a flash memory device,or other non-volatile solid state memory devices.

The input unit 1030 may be configured to receive an entered numeral orcharacter information, and generate key signal input related to usersetting and function control of the mobile phone. Specifically, theinput unit 1030 may include a touch panel 1031 and other input devices1032. The touch panel 1031, also referred to as a touchscreen, maycollect a touch operation performed by a user on or near the touchpanel, such as an operation performed by a user on the touch panel 1031or near the touch panel 1031 by using any proper object or accessory,such as a finger or a stylus. The touch panel can further drive acorresponding connecting apparatus according to a preset program.Optionally, the touch panel 1031 may include two parts, a touchdetection apparatus and a touch controller. The touch detectionapparatus detects a touch position of a user, detects a signal generatedby the touch operation, and transfers the signal to the touchcontroller. The touch controller receives the touch information from thetouch detection apparatus, converts the touch information into touchpoint coordinates, and transmits the touch point coordinates to theprocessor 1080. Moreover, the touch controller can receive and execute acommand sent from the processor 1080. In addition, the touch panel 1031may be a touch panel of a resistive, capacitive, infrared, or surfaceacoustic wave type. In addition to the touch panel 1031, the input unit1030 may further include another input device 1032. Specifically, theanother input device 1032 may include, but is not limited to, one ormore of a physical keyboard, a function key including a volume controlkey or a power on/off key, a trackball, a mouse, a joystick, and thelike.

The display unit 1040 may be configured to display information enteredby a user or information provided for the user, and various menus of themobile phone. The display unit 1040 may include a display panel 1041.Optionally, the display panel 1041 may be configured by using a liquidcrystal display (LCD), an organic light-emitting diode (OLED), and thelike. Further, the touch panel 1031 may cover the display panel 1041.After detecting a touch operation on or near the touch panel 1031, thetouch panel 1031 transfers the touch operation to the processor 1080, todetermine a type of a touch event. Then, the processor 1080 provides acorresponding visual output on the display panel 1041 according to thetype of the touch event. Although, in FIG. 9 , the touch panel 1031 andthe display panel 1041 are used as two separate parts to implement inputand output functions of the mobile phone, in some embodiments, the touchpanel 1031 and the display panel 1041 may be integrated to implement theinput and output functions of the mobile phone.

The mobile phone may further include at least one sensor 1050 such as anoptical sensor, a motion sensor, and other sensors. Specifically, theoptical sensor may include an ambient light sensor and a proximitysensor. The ambient light sensor may adjust luminance of the displaypanel 1041 according to brightness of the ambient light. The proximitysensor may switch off the display panel 1041 and/or backlight when themobile phone is moved to the ear. As one type of motion sensor, anacceleration sensor can detect magnitude of accelerations in variousdirections generally on three axes, may detect magnitude and a directionof the gravity when static, and may be applied to an application thatrecognizes the attitude of the mobile phone, for example, switchingbetween landscape orientation and portrait orientation, a related game,and magnetometer attitude calibration, a function related to vibrationrecognition, such as a pedometer and a knock, and the like. Othersensors, such as a gyroscope, a barometer, a hygrometer, a thermometer,and an infrared sensor, which may be configured in the mobile phone, arenot further described herein.

The audio circuit 1060, a speaker 1061, and a microphone 1062 mayprovide audio interfaces between the user and the mobile phone. Theaudio circuit 1060 may convert received audio data into an electricalsignal and transmit the electrical signal to the speaker 1061. Thespeaker 1061 converts the electrical signal into a sound signal foroutput. On the other hand, the microphone 1062 converts a collectedsound signal into an electrical signal. The audio circuit 1060 receivesthe electrical signal, converts the electrical signal into audio data,and outputs the audio data to the processor 1080 for processing. Then,the processor 1080 transmits the audio data to, for example, anothermobile phone by using the RF circuit 1010, or outputs the audio data tothe memory 1020 for further processing.

Wi-Fi belongs to a short distance wireless transmission technology. Themobile phone may help, by using the Wi-Fi module 1070, a user to receiveand send an email, browse a web page, access stream media, and the like.This provides wireless broadband Internet access for the user. AlthoughFIG. 9 shows the Wi-Fi module 1070, it may be understood that the Wi-Fimodule 1070 is not a necessary component of the mobile phone, and whenrequired, the Wi-Fi module 1070 may be omitted without changing thescope of the essence of the present disclosure.

As a control center of the mobile phone, the processor 1080 is connectedto all parts of the entire mobile phone by using various interfaces andlines, and performs various functions and data processing of the mobilephone by running or executing the software program and/or module storedin the memory 1020 and invoking the data stored in the memory 1020, toperform overall monitoring on the mobile phone. Optionally, theprocessor 1080 may include one or more processing units. Preferably, theprocessor 1080 may integrate an application processor and a modem. Theapplication processor mainly processes an operating system, a userinterface, and an application program and the like, and the modem mainlyprocesses wireless communication. It may be understood that theforegoing modem may alternatively not be integrated into the processor1080.

The mobile phone further includes the power supply 1090 (such as abattery) for supplying power to the components. Preferably, the powersupply may be logically connected to the processor 1080 by using a powermanagement system, thereby implementing functions such as charging,discharging, and power consumption management by using the powermanagement system. Although not shown in the figure, the mobile phonemay further include a camera, a Bluetooth module, and the like, whichare not described herein.

In an embodiment of this application, the processor 1080 included in theterminal further controls and performs a procedure of a colon polypimage processing method performed by the terminal.

FIG. 10 is a schematic structural diagram of a server according to anexemplary embodiment of this application. The server 1100 may varygreatly due to different configurations or performance, and may includeone or more central processing units (CPU) 1122, for example, one ormore processor, and a memory 1132, and one or more storage medium 1130,for example, one or more mass storage devices, that store applicationprograms 1142 or data 1144. The memory 1132 and the storage medium 1130may be transient storage or non-transitory permanent storage. Theprogram stored in the storage medium 1130 may include one or moremodules (not shown), and each module may include a series ofinstructions and operations for the server. Further, the CPU 1122 may beset to communicate with the storage medium 1130, and perform, on theserver 1100, the series of instruction operations in the storage medium1130.

The server 1100 may further include one or more power supplies 1126, oneor more wired or wireless network interfaces 1150, one or moreinput/output interfaces 1158, and/or one or more operating systems 1141,for example, Windows Server™, Mac OS X™, Unix™, Linux™, or FreeBSD™.

The steps of the colon polyp image processing method performed by theserver in the foregoing embodiment may be based on the server structureshown in FIG. 10 .

In addition, the described apparatus embodiment is merely an example.The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected according toactual needs to achieve the objectives of the solutions of theembodiments. Besides, in the accompanying drawings of the apparatusembodiments of this application, a connection relationship betweenmodules indicates a communication connection between them, and can bespecifically implemented as one or more communications buses or signallines. A person of ordinary skill in the art may understand andimplement the embodiments of this application.

According to the descriptions in the foregoing implementations, a personskilled in the art may clearly understand that the embodiments of thisapplication may be implemented by software and necessary generalhardware, and certainly can also be implemented by specific hardwareincluding an application-specific integrated circuit, a specific CPU, aspecific memory, a specific component, and the like. Generally, anyfunction implemented by a computer program can be easily implemented bycorresponding hardware, and specific hardware structures forimplementing the same function may be various. The structures may be ananalog circuit, a digital circuit, a specific circuit, or the like.However, for the embodiments of this application, the implementation bya software program is the better one in more cases. Based on such anunderstanding, the technical solutions in the embodiments of thisapplication essentially or the part contributing to the related art maybe implemented in a form of a software product. The computer softwareproduct is stored in a non-transitory computer readable storage medium,such as a floppy disk of a computer, a USB flash drive, a removable harddisk, a read-only memory (ROM), a random access memory (RAM), a magneticdisk, or an optical disc and includes several instructions forinstructing a computer device, which may be a personal computer, aserver, a network device, or the like, to perform the method describedin the embodiments of this application.

In summary, the foregoing exemplary embodiments are merely intended fordescribing the technical solutions of the embodiments of thisapplication, but not for limiting this application. Although theembodiments of this application are described in detail with referenceto the foregoing embodiments, it is to be understood by a person ofordinary skill in the art that they may still make modifications to thetechnical solutions described in the foregoing embodiments or makeequivalent replacements to some technical features thereof, withoutdeparting from the spirit and scope of the technical solutions of theembodiments of this application.

In an embodiment of this application, a position of a polyp in anendoscopic image is first detected by using a polyp positioning model,and a polyp image block is positioned in the endoscopic image, where thepolyp image block includes a position region of the polyp in theendoscopic image. Finally, a polyp type classification detection isperformed on the polyp image block by using a polyp propertyidentification model, and an identification result is outputted. In theembodiments of this application, because the position of the polyp isdetected by using the polyp positioning model, the polyp image block maybe directly positioned in the endoscopic image. The classificationdetection for the polyp type is also performed on the polyp image block,and does not need to be performed on the entire endoscopic image.Therefore, the real-time performance meets requirements. When theendoscope is controlled to move, an identification result of an imageacquired in real time can be outputted in real time, thereby improvingprocessing efficiency of the polyp image.

What is claimed is:
 1. A colon polyp image processing method,comprising: classifying a value of a blood vessel color feature in anendoscopic image by using an image classification model trained using aneural network algorithm to determine that the endoscopic image is awhite light type picture or an endoscope narrow band imaging (NBI) typepicture; detecting, by processing circuitry of a colon polyp imageprocessing apparatus, a polyp in the endoscopic image by using a polyppositioning model based on the determination that the endoscopic imageis the white light type picture or the NBI type picture; and performinga polyp type classification detection on the detected polyp in theendoscopic image by using a polyp property identification model, andoutputting an identification result.
 2. The method according to claim 1,wherein, before the detecting, the method further comprises: extractingat least one of a color feature, a gradient variation feature, or anabnormal brightness feature from the endoscopic image; determiningwhether the endoscopic image is a low-quality picture according to theat least one of the color feature, the gradient variation feature, orthe abnormal brightness feature, wherein the low-quality picture is oneof a blurred picture, an overexposed/underexposed picture with abnormaltone, or a low-resolution picture; and triggering a detecting of thepolyp in the endoscopic image by using the polyp positioning model whenthe endoscopic image is not the low-quality picture.
 3. The methodaccording to claim 1, wherein the detecting the polyp in the endoscopicimage comprises: detecting a position of the polyp in the endoscopicimage based on the determination that the endoscopic image is the whitelight type picture or the NBI type picture; and positioning a polypimage block at the position of the polyp in the endoscopic image, thepolyp image block being a subregion in the endoscopic image.
 4. Themethod according to claim 3, wherein the classifying further comprises:performing classification training on an original image classificationmodel through white light type picture training data and NBI typepicture training data by using the neural network algorithm to obtainthe trained image classification model; and extracting the blood vesselcolor feature from the endoscopic image by using the trained imageclassification model.
 5. The method according to claim 3, the polyppositioning model comprising a white light polyp positioning model andan NBI polyp positioning model, wherein: the white light polyppositioning model is obtained by the colon polyp image processingapparatus performing polyp position training on an original polyppositioning model through white light type picture training data byusing a neural network algorithm, and the NBI polyp positioning model isobtained by the colon polyp image processing apparatus performing polypposition training on the original polyp positioning model through NBItype picture training data by using the neural network algorithm.
 6. Themethod according to claim 5, wherein the detecting the position of thepolyp further comprises: performing polyp positioning by using the whitelight polyp positioning model when the endoscopic image is determined asthe white light type picture to position a white light polyp image blockin the endoscopic image; and performing polyp positioning by using theNBI polyp positioning model when the endoscopic image is determined asthe NBI type picture to position an NBI polyp image block in theendoscopic image.
 7. The method according to claim 3, wherein theperforming further comprises: performing polyp type classificationdetection training on an original polyp property identification modelthrough polyp picture training data of different polyp types by using aneural network algorithm to obtain a trained polyp propertyidentification model; extracting a polyp type feature from the polypimage block by using the trained polyp property identification model;and classifying a value of the polyp type feature by using the trainedpolyp property identification model and outputting the identificationresult.
 8. The method according to claim 7, wherein, after thepositioning the polyp image block in the endoscopic image, the methodfurther comprising: expanding a polyp region occupied by the polyp imageblock on the endoscopic image upwards, downwards, leftwards, andrightwards according to a preset image expansion ratio to obtain anexpanded polyp image block; and inputting the expanded polyp image blockinto the polyp property identification model.
 9. A colon polyp imageprocessing apparatus, comprising: processing circuitry configured to:classify a value of a blood vessel color feature in an endoscopic imageby using an image classification model trained using a neural networkalgorithm to determine that the endoscopic image is a white light typepicture or an endoscope narrow band imaging (NBI) type picture; detect apolyp in the endoscopic image by using a polyp positioning model basedon the determination that the endoscopic image is the white light typepicture or the NBI type picture; and perform a polyp type classificationdetection on the detected polyp in the endoscopic image by using a polypproperty identification model and output an identification result. 10.The apparatus according to claim 9, wherein the processing circuitry isfurther configured to: extract at least one of a color feature, agradient variation feature, or an abnormal brightness feature from theendoscopic image before the polyp in the endoscopic image is detected byusing the polyp positioning model; determine whether the endoscopicimage is a low-quality picture according to the at least one of thecolor feature, the gradient variation feature, or the abnormalbrightness feature, wherein the low-quality picture is one of a blurredpicture, an overexposed/underexposed picture with abnormal tone, or alow-resolution picture; and trigger the detection of the polyp in theendoscopic image by using the polyp positioning model when theendoscopic image is not the low-quality picture.
 11. The apparatusaccording to claim 9, wherein the processing circuitry is furtherconfigured to: detect a position of the polyp in the endoscopic imagebased on the determination that the endoscopic image is the white lighttype picture or the NBI type picture; and position a polyp image blockat the position of the polyp in the endoscopic image, the polyp imageblock being a subregion in the endoscopic image.
 12. The apparatusaccording to claim 11, wherein the processing circuitry is furtherconfigured to: perform classification training on an original imageclassification model through white light type picture training data andNBI type picture training data by using the neural network algorithm toobtain the trained image classification model; and extract the bloodvessel color feature from the endoscopic image by using the trainedimage classification model.
 13. The apparatus according to claim 11, thepolyp positioning model including a white light polyp positioning modeland an NBI polyp positioning model, wherein: the white light polyppositioning model is obtained by performing polyp position training onan original polyp positioning model through white light type picturetraining data by using a neural network algorithm, and the NBI polyppositioning model is obtained by performing polyp position training onthe original polyp positioning model through NBI type picture trainingdata by using the neural network algorithm.
 14. The apparatus accordingto claim 13, wherein the processing circuitry is further configured to:perform polyp positioning by using the white light polyp positioningmodel when the endoscopic image is determined as the white light typepicture to position a white light polyp image block in the endoscopicimage; and perform polyp positioning by using the NBI polyp positioningmodel when the endoscopic image is determined as the NBI type picture toposition an NBI polyp image block in the endoscopic image.
 15. A medicalsystem, comprising an endoscope apparatus, the colon polyp imageprocessing apparatus according to claim 9, and a communicationconnection between the endoscope apparatus and the colon polyp imageprocessing apparatus, wherein: the endoscope apparatus is configured togenerate an endoscopic video stream and transmit the generatedendoscopic video stream to the colon polyp image processing apparatus,and the colon polyp image processing apparatus is configured to receivethe endoscopic video stream from the endoscope apparatus, and obtain theendoscopic image from the endoscopic video stream.
 16. An imageprocessing method, comprising: classifying a value of a blood vesselcolor feature in an image by using an image classification model trainedusing a neural network algorithm to determine that the image is a firsttype picture or a second type picture; detecting, by an image processingapparatus, a target object in the image by using a target objectpositioning model based on the determination that the image is the firsttype picture or the second type picture; and performing a target objecttype classification detection on the detected target object in the imageby using a target object property identification model and outputting anidentification result.
 17. The method according to claim 16, wherein,before the detecting, the method further comprises: extracting at leastone of a color feature, a gradient variation feature, or an abnormalbrightness feature from the image; determining whether the image is alow-quality picture according to the at least one of the color feature,the gradient variation feature, or the abnormal brightness feature,wherein the low-quality picture is one of a blurred picture, anoverexposed/underexposed picture with abnormal tone, or a low-resolutionpicture; and triggering, when the image is not the low-quality picture,the detecting the target object in the image by using the target objectpositioning model.
 18. The method according to claim 16, wherein thedetecting the target object in the image comprises: detecting a positionof the target object in the image based on the determination that theimage is the first type picture or the second type picture; andpositioning a target object image block at the position of the targetobject in the image, the target object image block being a subregion inthe image.
 19. The method according to claim 18, wherein the classifyingfurther comprises: performing classification training on an originalimage classification model through white light type picture trainingdata and NBI type picture training data by using the neural networkalgorithm to obtain a trained image classification model; and extractingthe blood vessel color feature from the image by using the trained imageclassification model.
 20. The method according to claim 18, wherein thetarget object positioning model comprises a white light target objectpositioning model and an NBI target object positioning model, wherein:the white light target object positioning model is obtained by the imageprocessing apparatus performing target object position training on anoriginal target object positioning model through white light typepicture training data by using a neural network algorithm; and the NBItarget object positioning model is obtained by the image processingapparatus performing target object position training on the originaltarget object positioning model through NBI type picture training databy using the neural network algorithm.